At least partial automation is being applied in a growing range of fields, improving the efficiency of industrial processes and raising productivity in a dramatic way. This improvement has not been fully matched in the service sector, in part because many of the tasks carried out in the services industry are perceived to require intelligent reasoning and skills that are not easily emulated by machines.
This is particularly true in finance where fundamental analysis has been a human function, and traditional technical analysis has also relied on humans to interpret results. Many technical analysis tools are available, but these are often relatively unsophisticated systems that are viewed as “toys” by more quantitatively-minded analysts. While there are comparatively better systems that provide a large amount of potentially useful information, these systems are not true predictive systems, because they ultimately rely on heuristic rules that cannot have global validity over time in a constantly changing financial Market.
A large part of finance is associated with trying to predict what will happen in the future. Both fundamental analysis and technical analysis are concerned with the question of prediction of the price of a financial instrument, but approach it from completely different points of view. Traditional economic theory (the Efficient Markets hypothesis) states that prices efficiently encode the available information about an instrument. An oft stated corollary of this is that price movements in markets are unpredictable in that past behavior of price cannot be used to predict future movements. Mounting evidence, however, suggests that markets are not completely efficient or rational. Any system that can take advantage of such inefficiencies might be capable of permanent profit making.
Unfortunately, inefficiencies are neither linear nor permanent in character. Additionally, the exploitation of an inefficiency can eventually destroy it. If, for instance, a trader exploits an inefficiency using a particular trading strategy then, due to potential information leakage during trading, other market participants can learn about the strategy employed by the trader, and hence the inefficiency, and can subsequently exploit or “arbitrage” it by adapting their own trading strategies in the light of this learned information, thus contributing to the disappearance of the inefficiency. Consequently, a particular financial instrument may only temporarily exhibit a “nonrandom” or “predictable” behavior. Although bubbles of predictability in price movements may quickly disappear through arbitrage, other important financial variables, such as liquidity and market impact, may exhibit more and/or longer lived predictability, especially if they do not obviously lead to profit making opportunities.
Liquidity, in particular, may be of great interest for trades involving large quantities of financial instruments. For instance, with larger “block” trades that are carried out with regard to taking advantage of medium- to long-term price movements, liquidity may be the main determinant of execution performance. A typical situation involves a Portfolio Manager deciding to implement a repositioning of the portfolio. Such repositioning for a large portfolio typically involves large volumes. The Portfolio Manager before requesting that the stock be traded within the boundaries of certain parameters, such as the execution of the trade over a specified time period, wishes to estimate the potential market impact associated with the trade. This is Transaction Cost Analysis (TCA). If potential market impact is high the Portfolio Manager may choose to postpone the trade. To optimize the trade, it may be necessary not only to determine the optimal timeframe for execution of that trade, e.g., the timeframe in which there is sufficient liquidity to support the trade within a specified price range, but also at which particular moments to effect a trade. Current systems for estimating market impact do so based on non-adaptive models that are independent of the trading strategy adopted by the trader and other market participants. However, market impact, depending on liquidity and information leakage, consequently depends on the trading strategies of other market participants as well as the trader's own strategy. An ideal strategy, if feasible, would be for a trader to be able to identify a counterparty who wished to trade the same or similar amount of an instrument and at a price that was mutually acceptable. In reality, and especially in the context of block trades, it may be difficult if not impossible to find such an ideal counterparty.
Difficulties relating to finding an ideal counterparty may be exacerbated by the leakage of information that a party wishes to trade. For example, rumors that a trader wishes to effect a large trade may be exploited by other traders to their own benefit and at the expense of the original trader. Thus, in the real world, a trader may need to protect against the leakage of information that could be exploited by other traders.
Several systems, such as the electronic exchange of Pipeline Trading Systems, are intended to guarantee anonymity and try to match buyers and sellers of large blocks. Systems featuring anonymity and/or matchmaking methodology may have at least two drawbacks. First, liquidity may not be sufficient to provide a high probability of finding a counterparty at any given time. Second, even if a counterparty is found, that counterparty may not be interested or capable of trading the large block in its entirety. In such scenarios, the “excess” must then be traded using a different methodology and often under undesirable conditions.
At least two aspects contribute to optimal trading in this scenario: time and price. A trader cannot wait an unlimited amount of time to execute the trades. Also, a trader must try and obtain an optimal price for trades, and this involves both liquidity and information leakage. These in their turn depend on the trading strategies of other market participants.
A third factor is that optimal trade execution must be obtained potentially across many different financial instruments simultaneously and in real time. Such requirements have opened the door for algorithmic trading engines. Existing trading algorithms, however, tend to be rules-based and non-adaptive.
Artificial Intelligence is a relative newcomer to the field of finance. Many systems for prediction of price movements, however, contain sophisticated elements, such as neural networks and genetic algorithms. These systems may apply highly non-linear analysis and use computationally complex processes whose results can be highly unstable. Additionally, they do not necessarily offer predictions based on established rules, but need to be “tuned” or “trained” by the user, who almost inevitably is not an expert in artificial intelligence and, therefore, likely to produce unreliable results. If the “tuning” or “training” is successful, the producer of the system can claim credit, and if unsuccessful the producer can blame the client for not training the system well enough. Additionally, training of neural networks or optimization in genetic algorithms, if done correctly, tend to be computationally intensive processes requiring computational resources and resources of time from the client that could better be dedicated to other tasks.
On the forefront of artificial intelligence research are intelligent artificial agent systems, which are now opening new avenues for productivity increases in areas where humans are carrying out repetitive intelligent tasks. Commercial applications of intelligent agents have essentially been restricted to “data mining” where a more intelligent search of databases is carried out. In fact, many such systems are no more sophisticated than standard web search engines.